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Land salinization or its reversion, desalinization, is a complex process affected by both biophysical and human-induced driving factors. Conventional approaches to land salinization assessment and simulation are either too time consuming or focused only on biophysical factors. The cellular automata (CA)-Markov model is well suited for regional-scale assessment and simulation since both biophysical and socioeconomic data can be efficiently incorporated into a GIS framework. Through a case study of Yinchuan Plain in northwest China we demonstrated that the CA-Markov model can be effectively used to model the spatial and temporal dynamics of the salt- affected landscape changes caused by the underlying salinization/desalinization process. Our research showed that Yinchuan Plain had been in the process of desalinization in the past ten years when the salt-affected lands decreased about 11%, mainly due to the construction and improvement of irrigation and drainage channel networks. CA-Markov model performed better in simulating salt-affected landscape changes when accounting for both biophysical and human-induced driving factors that affected the land salinization/desalinization processes in Yinchuan Plain. The CA-Markov model simulation results showed that the desalinization trend in Yinchuan Plain would continue into the future. By 2015, the total salt-affected lands would be further reduced to about 26.7 %. Our findings not only shed light on the causes behind the salt-affected landscape changes in Yinchuan Plain in the past decade, but also established the CA-Markov model as a promising tool for land salinization assessment and simulation at a regional scale.